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NEW QUESTION # 128
You have an Azure subscription.
You need to use Azure Content Understanding in Foundry Tools to extract structured data from invoices.
What should you provision?
Answer: B
Explanation:
To use Azure Content Understanding in Foundry Tools, Microsoft lists a Microsoft Foundry resource as a prerequisite. The documentation states that you need a Microsoft Foundry resource created in a supported region, and that the portal lists this resource under Foundry > Foundry.
The invoice scenario is also directly aligned with Content Understanding's intelligent document processing use case: Microsoft states that Content Understanding converts unstructured documents into structured data and gives invoice processing as an example.
Therefore, to extract structured data from invoices by using Azure Content Understanding in Foundry Tools, you should provision a Microsoft Foundry resource.
NEW QUESTION # 129
For each of the following statements, select Yes if the statement is true Otherwise, select No NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* No
* Yes
* No
Comprehensive and Detailed Explanation with all Azure AI documents : =
Statement 1: Speech synthesis converts spoken audio into written text. = No Speech synthesis, also called text to speech , converts written text into spoken audio. Converting spoken audio into written text is speech recognition or speech to text .
Statement 2: Speech recognition can detect and transcribe spoken input. = Yes Speech recognition is used to identify spoken language in audio and convert it into text.
Statement 3: Text to speech in Azure Speech in Foundry Tools can translate text input into audible speech in a different language. = No Text to speech converts text into spoken audio. Translation into another language is a separate translation capability, not standard text-to-speech synthesis.
NEW QUESTION # 130
For each of the following statements, select Yes if the statement is true, Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement
Answer
Generative AI models predict the next token in a sequence based on patterns learned during training.
Yes
A system prompt can be used to influence the behavior and tone of the responses generated by a generative AI model.
Yes
Increasing the Temperature parameter makes the responses generated by generative AI models more deterministic and consistent.
No
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide
/topics:
The first statement is Yes. Generative language models generate text autoregressively by estimating probabilities for the next token based on preceding context and patterns learned during model training. This next-token prediction mechanism is fundamental to how transformer-based generative models produce sequences of text.
The second statement is also Yes. A system prompt provides high-level instructions that establish how the model should behave. It can define role, response style, tone, constraints, and other behavioral expectations.
The AI-901 study guide explicitly includes creating effective system and user prompts as part of implementing generative AI applications in Microsoft Foundry.
The third statement is No. Increasing Temperature does the opposite of making output more deterministic.
Microsoft documents that higher temperature values increase randomness and output diversity, while lower values make responses more focused and predictable. At a temperature approaching zero, model selection becomes substantially more deterministic because higher-probability tokens are favored more strongly.
NEW QUESTION # 131
For each of the following statement1, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement 1: Voice Live returns only transcribed text. = No
Voice Live is not limited to returning transcribed text. It is designed for real-time spoken conversational experiences and can include speech input, model reasoning, and spoken output.
Statement 2: Voice Live requires you to separately implement speech to text and text to speech services. = No Voice Live combines the conversational voice pipeline, so you do not need to separately build independent speech-to-text and text-to-speech services for the same real-time voice experience.
Statement 3: Voice Live combines speech to text, reasoning, and text to speech into a single conversational experience. = Yes This statement is correct. Voice Live is used for real-time conversational AI experiences where spoken input is processed, the model reasons over the request, and spoken output can be returned.
NEW QUESTION # 132
You are developing a simple application that uses the Microsoft Foundry SDK to send chat prompts to a model.
Which three elements should you provide in the code to run the application? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D,E
Explanation:
To build a simple application that sends chat prompts to a model using the Microsoft Foundry SDK, you will initialize a project client using your endpoint and credentials, then request chat completions using your model deployment name.
Microsoft Entra ID Credentials: Handled via DefaultAzureCredential(). This eliminates the need to hardcode sensitive API keys. Before running this app, ensure you are authenticated in your terminal by running az login via the Azure CLI. Your account must have the Cognitive Services User or Azure AI Developer role assigned.
Endpoint URL: Passed directly into the AIProjectClient initializer. This maps your code to the specific Azure AI Foundry project hub managing your computational resources.
Model Deployment Name: Referenced inside the final .create() method payload. This instructs the inference routing service exactly which deployed LLM (e.g., GPT-4o, DeepSeek, or Claude) should process your incoming chat transcript.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/chatgpt
NEW QUESTION # 133
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